Indoor-outdoor detector for estimating the location of a wireless terminal

ABSTRACT

A disclosed detector estimates the wireless terminal as being indoors or outdoors by utilizing information that includes measurement report data from previous calls involving both i) wireless terminals that are known to have been indoors and ii) wireless terminals that are known to have been outdoors while the measurement report data was collected. Based on this call data, one or more classification features are computed in accordance with the illustrative embodiment of the present invention. Then, for a wireless terminal that is to be classified during a call and with respect to the terminal being either indoors or outdoors, features that are representative of that wireless terminal for the call are evaluated against a characterization that is a composite of one or more previously-computed classification features. The features that are representative of the wireless terminal can be derived from the measurement reports received from the wireless terminal during the call.

FIELD OF THE INVENTION

The present invention relates to telecommunications in general, and,more particularly, to a technique for determining an estimate of thelocation of a wireless terminal based on whether the wireless terminalis detected as being indoors or outdoors.

BACKGROUND OF THE INVENTION

The salient advantage of wireless telecommunications over wirelinetelecommunications is the user of the wireless terminal is afforded theopportunity to use his or her terminal anywhere. On the other hand, thesalient disadvantage of wireless telecommunications lies in that factthat because the user is mobile, an interested party might not be ableto readily ascertain the location of the user.

Such interested parties might include both the user of the wirelessterminal and a remote party. There are a variety of reasons why the userof a wireless terminal might be interested in knowing his or herlocation. For example, the user might be interested in telling a remoteparty where he or she is or, alternatively, the user might seek advicein navigation.

In addition, there are a variety of reasons why a remote party might beinterested in knowing the location of the user. For example, therecipient of an E 9-1-1 emergency call from a wireless terminal might beinterested in knowing the location of the wireless terminal so thatemergency services vehicles can be dispatched to that location.

There are many techniques in the prior art for estimating the locationof a wireless terminal. In accordance with some techniques, the locationof a wireless terminal is estimated, at least in part, from signalmeasurements that are reported by the wireless terminal. The reportedmeasurements are of signals measured by the wireless terminal that aretransmitted by one or more base stations and, in some cases, by GlobalPositioning System (GPS) satellites. In order for these techniques towork, at least some of the transmitted signals have to be strong enoughto allow for accurate measurement by the wireless terminal and forreliable processing by the particular estimation technique. Some ofthese techniques work well even in environments where the measuredstrengths of the different signals vary significantly, such as wheresignal obstructions are present, including natural obstructions such asmountains and artificial obstructions such as buildings.

In some environments, however, signals that are too weak to be usableand environmental conditions that are insufficiently or incorrectlycharacterized can cause at least some location estimation techniques toproduce unreliable location estimates. For example, some indoorenvironments can cause such problems to occur.

SUMMARY OF THE INVENTION

There are a number of systems in the prior art for estimating thelocation of a wireless terminal. Several of the factors that affect theaccuracy of the estimate are:

-   -   1. whether the signals that travel to and from the wireless        terminal are impaired (e.g., attenuated, reflected, refracted,        etc.) or not,    -   2. whether the system knows if the signals have been impaired or        not, and    -   3. whether the system compensates for the impairment or not.

When the system knows that the signals have been impaired andcompensates for the impairment, the accuracy of the estimate can be verygood. In contrast, when the system does not know that the signals havebeen impaired or does not compensate for the impairment, the accuracy ofthe estimate can be very bad. The military, police, and emergencyservices often rely on the estimates to be good, and a bad estimate canhave serious consequences.

Signals can be impaired by natural objects such as mountains and byman-made objects such as buildings. The impairment caused when awireless terminal is indoors is particularly insidious, and it isdifficult in the prior art to know that the wireless terminal isindoors.

To address this problem, embodiments of the present invention estimatewhether a wireless terminal is indoors or outdoors. Although it istrivial for a human to know whether he or she is indoors or outdoors,and it might seem that it should be simple for a machine to know whetherit is indoors or not, it has been a difficult problem.

Embodiments of the present invention estimate whether the wirelessterminal is indoors or outdoors by utilizing information for past callsthat is readily available in at least some wireless telecommunicationssystems. This call data includes measurement report data from past callsinvolving both i) wireless terminals that are known to have been indoorsand ii) wireless terminals that are known to have been outdoors whilethe measurement report data was collected. Based on this call data, oneor more classification features are computed in accordance with theillustrative embodiment of the present invention. These features arerelated to the signal-strength levels of neighbor cells, the number ofneighbor cells being reported, and the size of neighbor-cell coverageareas, for example and without limitation. Then, for a particularwireless terminal that is to be classified during a particular call interms of it being indoors or outdoors, the features that arerepresentative of that wireless terminal for the call are evaluatedagainst a characterization that is a composite of one or more of thepreviously-computed classification features. The features that arerepresentative of the wireless terminal can be derived from themeasurement reports received from the wireless terminal during the call.In some embodiments of the present invention, an estimate of theprobability that the wireless terminal is indoors (or outdoors) isgenerated.

Once it has been determined that the wireless terminal is probablyindoors or probably outdoors, this information can be used accordingly.For example, the cost of generating a location estimate—in terms oftime, hardware, money, compute cycles, and energy (e.g., electrical,etc.)—depends on the technique and on the quantity and quality of theempirical data. There are some techniques in which the cost of alocation estimate can vary widely based on the quantity and quality ofthe empirical data. This is particularly true for pattern-matchingtechniques such as Radio-Frequency Pattern Matching. For thesetechniques, it is advantageous to employ, when possible, mechanisms thatlower the average (or maximum) cost of an estimate. The presentinvention, as recited in the claims, is one such mechanism.

In particular, it is possible to reduce the cost of estimating alocation, by recognizing that some estimates of the location of awireless terminal are improbable. For example, if a wireless terminal isestimated to be indoors, then one or more outdoor locations can be ruledout as improbable. Thus, the illustrative embodiment lowers the cost ofthe location estimate by quickly eliminating the need to expendresources to consider some, or all, outdoor locations when it is likelythat the wireless terminal is indoors. Furthermore, the illustrativeembodiment can improve the accuracy of the location estimate byeliminating the possibility of making an error by estimating, in theexample, the location of the wireless terminal to be a particular placeoutdoors when it is actually indoors.

As another example of reducing the cost of estimating a location, if awireless terminal is estimated instead to be outdoors, then the terminalcan be assumed to be at or near ground level. This provides a positionconstraint along the vertical axis, thereby simplifying the locationestimation process. In contrast, a wireless terminal that is estimatedto be indoors might be, for example, on any of the floors in a high-risebuilding (i.e., unconstrained along the vertical axis).

As yet another example of reducing the cost of estimating a location, ifa wireless terminal is estimated to be indoors, then equipment such asthe GPS radio in the wireless terminal can be powered off (or keptpowered off) because it might not be of any use indoors. In this way,energy usage (e.g., battery consumption in the wireless terminal, etc.)is minimized, thereby lowering cost and improving performance (e.g., byextending battery life, etc.) in the system.

Additionally, in regard to location-based applications, differentbehaviors can be applied depending on whether the wireless terminal—and,therefore, its user—is estimated to be indoors or outdoors. For example,an application might only want to serve an advertisement when a personenters a shop, but not before or if the person is simply walking past.As another example, an application might want to instruct a securityofficer whether to look in an outdoor crowd or inside a nearby shop fora person of interest. The detector disclosed herein supports thesescenarios of location-based applications and others.

An illustrative method of estimating the location of a wireless terminalcomprises: receiving, by a server computer, the identities of one ormore radio signals that are received by the wireless terminal; andestimating, by the server computer, a probability that the wirelessterminal is indoors based on i) the identities of the one or more radiosignals that are received by the wireless terminal and ii) acharacterization that is based on the amount of unique identities thathave appeared over a predetermined interval, wherein the uniqueidentities are of radio signals that have been received by a pluralityof wireless terminals.

Another method of estimating the location of a wireless terminalcomprises: receiving, by a server computer, the identity of a radiosignal that is received by the wireless terminal; receiving, by theserver computer, a measurement of a location-dependent trait of a radiosignal as received by the wireless terminal; and estimating, by theserver computer, a probability that the wireless terminal is indoorsbased on i) a characterization of a first classification feature,wherein the characterization is based on multiple measurement reportsthat are transmitted by a plurality of wireless terminals and accountsfor both known indoor calls and known outdoor calls, and ii) a value ofthe first classification feature, wherein the value of the firstclassification feature corresponds to the identity of the radio signaland is representative of the wireless terminal; and generating, by theserver computer, an estimate of the location of the wireless terminalbased on i) the measurement of the location-dependent trait of the radiosignal and ii) the estimated probability that the wireless terminal isindoors.

Yet another method of estimating the location of a wireless terminalcomprises: receiving, by a server computer, the identity of a radiosignal that is received by the wireless terminal; receiving, by theserver computer, a measurement of a location-dependent trait of a radiosignal as received by the wireless terminal; estimating, by the servercomputer, a probability that the wireless terminal is indoors based oni) a characterization of a first classification feature, wherein thecharacterization is based on multiple measurement reports that aretransmitted by a plurality of wireless terminals, and ii) a value of thefirst classification feature, wherein the value of the firstclassification feature corresponds to the identity of the radio signaland is representative of the wireless terminal; designating, by theserver computer, at least one of a plurality of possible locations ofthe wireless terminal as improbable based on the estimated probabilitythat the wireless terminal is indoors; and generating, by the servercomputer, an estimate of the location of the wireless terminal as beingone of the plurality of possible locations of the wireless terminal notdesignated as improbable, based on the measurement of thelocation-dependent trait of the radio signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a diagram of the salient components of wirelesstelecommunications system 100 in accordance with the illustrativeembodiment of the present invention.

FIG. 2 depicts a block diagram of the salient components of locationengine 113 of system 100.

FIG. 3 depicts a radio frequency (RF) map that represents a partitioningof geographic region 120 of system 100 into 24 square locations.

FIG. 4 depicts a flowchart of the salient processes performed inaccordance with the illustrative embodiment of the present invention.

FIG. 5 depicts a flowchart of the salient processes performed inaccordance with task 401.

FIG. 6 depicts a flowchart of the salient processes performed inaccordance with task 403.

FIG. 7 depicts a flowchart of the salient processes performed inaccordance with task 601.

FIG. 8, depicts cumulative distribution functions of outdoor calls andindoor calls.

FIG. 9 depicts a flowchart of the salient processes performed inaccordance with task 405.

FIG. 10 depicts a flowchart of the salient processes performed inaccordance with task 407.

FIG. 11 depicts a flowchart of the salient processes performed in task409.

FIG. 12 depicts a flowchart of the salient processes performed inaccordance with task 1101.

FIG. 13 depicts a flowchart of the salient processes performed inaccordance with task 1103.

DETAILED DESCRIPTION

Based on—

For the purposes of this specification, the phrase “based on” is definedas “being dependent on” in contrast to “being independent of”. The valueof Y is dependent on the value of X when the value of Y is different fortwo or more values of X. The value of Y is independent of the value of Xwhen the value of Y is the same for all values of X. Being “based on”includes both functions and relations.

Estimate of the Probability that the Wireless Terminal is Indoors—

For the purposes of this specification, an “estimate of the probabilitythat the wireless terminal is indoors” is defined as the complement ofan estimate of the probability that the wireless terminal is outdoors(i.e., P(indoors)=1−P(outdoors)).

Generate—

For the purposes of this specification, the infinitive “to generate” andits inflected forms (e.g., “generating”, “generation”, etc.) should begiven the ordinary and customary meaning that the terms would have to aperson of ordinary skill in the art at the time of the invention.

Identity of a Radio Signal—

For the purposes of this specification, the phrase “identity of a radiosignal” is defined as one or more indicia that distinguish one radiosignal from another radio signal. Cell ID is an example of an identityof a radio signal.

Location—

For the purposes of this specification, the term “location” is definedas a zero-dimensional point, a finite one-dimensional path segment, afinite two-dimensional surface area, or a finite three-dimensionalvolume.

Location-Dependent Trait of a Radio Signal—

For the purposes of this specification, the term “location-dependenttrait of a radio signal” is defined as a characteristic of a radiosignal that varies with:

-   -   (i) the location of the transmitter of the signal, or    -   (ii) the location of the receiver of the signal, or    -   (iii) both i and ii.        For example and without limitation, the amplitude and phase of a        radio signal are generally location-dependent traits of the        signal. In contrast, the frequency of a radio signal is        generally not a location-dependent trait of the signal.

Location-Trait Database—

For the purposes of this specification, a “Location-Trait Database” isdefined as a mapping that associates:

-   -   (i) one or more location-dependent traits of one or more radio        signals received or transmitted by a wireless terminal, or    -   (ii) the identity of one or more radio signals received or        transmitted by a wireless terminal, or    -   (iii) both i and ii,        at each of a plurality of locations.

Processor—

For the purposes of this specification, a “processor” is defined ashardware or hardware and software that performs mathematical and/orlogical operations.

Radio—

For the purposes of this specification, a “radio” is defined as hardwareor hardware and software that is capable of telecommunications via anunguided (i.e., wireless) radio signal of frequency less than 600 GHz.

Receive—

For the purposes of this specification, the infinitive “to receive” andits inflected forms (e.g., “receiving”, “received”, etc.) should begiven the ordinary and customary meaning that the terms would have to aperson of ordinary skill in the art at the time of the invention.

Transmit—

For the purposes of this specification, the infinitive “to transmit” andits inflected forms (e.g., “transmitting”, “transmitted”, etc.) shouldbe given the ordinary and customary meaning that the terms would have toa person of ordinary skill in the art at the time of the invention.

Wireless Terminal—

For the purposes of this specification, the term “wireless terminal” isdefined as a device that is capable of telecommunications without a wireor tangible medium. A wireless terminal can be mobile or immobile. Awireless terminal can transmit or receive or transmit and receive. As iswell known to those skilled in the art, a wireless terminal is alsocommonly called a cell phone, a pager, a wireless transmit/receive unit(WTRU), a user equipment (UE), a mobile station, a fixed or mobilesubscriber unit, a pager, a cellular telephone, a personal digitalassistant (PDA), a computer, and any other type of device capable ofoperating in a wireless environment are examples of wireless terminals.

FIG. 1 depicts a diagram of the salient components of wirelesstelecommunications system 100 in accordance with the illustrativeembodiment of the present invention. Wireless telecommunications system100 comprises: wireless terminal 101, cellular base stations 102-1,102-2, and 102-3, Wi-Fi base stations 103-1 and 103-2, wirelessinfrastructure 111, location-based application server 112, locationengine 113, and GPS constellation 121, interrelated as shown.

Wireless infrastructure 111, location-based application server 112,location engine 113, and Wi-Fi base stations 103-1 and 103-2 are allconnected to one or more interconnected computer networks (e.g., theInternet, a local-area network, a wide-area network, etc.) and, as such,can exchange data in well-known fashion.

Although the illustrative embodiment depicts wireless telecommunicationssystem 100 as comprising only one wireless terminal, it will be clear tothose skilled in the art, after reading this disclosure, how to make anduse alternative embodiments of the present invention that comprise anynumber of wireless terminals.

Wireless terminal 101 comprises the hardware and software necessary toperform the processes described below and in the accompanying figures.Furthermore, wireless terminal 101 is mobile and can be at any locationwithin geographic region 120 at any time.

Wireless terminal 101 is capable of providing bi-directional voice,data, and video telecommunications service to a user (not shown), but itwill be clear to those skilled in the art, after reading thisdisclosure, how to make and use embodiments of the present invention inwhich wireless terminal 101 provides a different set of services.

In accordance with the illustrative embodiment, wireless terminal 101 iscapable of receiving one or more radio signals from each of basestations 102-1, 102-2, and 102-3, Wi-Fi base stations 103-1 and 103-2,and GPS constellation 121, in well-known fashion. Wireless terminal 101is also capable of identifying each radio signal it receives, inwell-known fashion, and of transmitting the identity of each signal itreceives to location engine 113. Wireless terminal 101 is furthercapable of measuring one or more location-dependent traits of each radiosignal it receives, in well-known fashion, and of transmitting eachmeasurement it generates to location engine 113. And still furthermore,wireless terminal 101 is capable of measuring a difference of alocation-dependent trait of two signals it receives, in well-knownfashion, and of transmitting such measurements to location engine 113.

In accordance with the illustrative embodiment, wireless terminal 101 iscapable of transmitting one or more radio signals—that can be receivedby one or more of base stations 102-1, 102-2, and 102-3 and Wi-Fi basestations 103-1 and 103-2—in accordance with specific parameters (e.g.,signal strength, frequency, coding, modulation, etc.), in well-knownfashion, and of transmitting those parameters to location engine 113.

Cellular base stations 102-1, 102-2, and 102-3 communicate with wirelessinfrastructure 111 via wireline and with wireless terminal 101 via radioin well-known fashion. As is well known to those skilled in the art,base stations are also commonly referred to by a variety of alternativenames such as access points, nodes, network interfaces, etc. Althoughthe illustrative embodiment comprises three base stations, it will beclear to those skilled in the art, after reading this disclosure, how tomake and use alternative embodiments of the present invention thatcomprise any number of base stations.

In accordance with the illustrative embodiment of the present invention,cellular base stations 102-1, 102-2, and 102-3 are terrestrial,immobile, and base station 102-3 is within geographic region 120. Itwill be clear to those skilled in the art, after reading thisdisclosure, how to make and use alternative embodiments of the presentinvention in which some or all of the base stations are airborne,marine-based, or space-based, regardless of whether or not they aremoving relative to the Earth's surface, and regardless of whether or notthey are within geographic region 120.

Cellular base stations 102-1, 102-2, and 102-3 comprise the hardware andsoftware necessary to be 3GPP-compliant and to perform the processesdescribed below and in the accompanying figures. For example and withoutlimitation, each of cellular base stations 102-1, 102-2, and 102-3 arecapable of continually:

-   -   a. receiving one or more radio signals transmitted by wireless        terminal 101, and    -   b. identifying each radio signal transmitted by wireless        terminal 101, in well-known fashion, and of transmitting the        identity of those signals to location engine 113, and    -   c. measuring one or more location-dependent traits of each radio        signal transmitted by wireless terminal 101, in well-known        fashion, and of transmitting the measurements to location engine        113, and    -   d. transmitting one or more signals to wireless terminal 101 in        accordance with specific parameters (e.g., signal strength,        frequency, coding, modulation, etc.), in well-known fashion, and        of transmitting those parameters to location engine 113.        It will be clear to those skilled in the art how to make and use        cellular base stations 102-1, 102-2, and 102-3.

Wi-Fi base stations 103-1 and 103-2 communicate with wireless terminal101 via radio in well-known fashion. Wi-Fi base stations 103-1 and 103-2are terrestrial, immobile, and within geographic region 120. Althoughthe illustrative embodiment comprises two Wi-Fi base stations, it willbe clear to those skilled in the art, after reading this disclosure, howto make and use alternative embodiments of the present invention thatcomprise any number of Wi-Fi base stations.

Each of Wi-Fi base stations 103-1 and 103-2 are capable of continually:

-   -   a. receiving one or more radio signals transmitted by wireless        terminal 101, and    -   b. identifying each radio signal transmitted by wireless        terminal 101, in well-known fashion, and of transmitting the        identity of those signals to location engine 113, and    -   c. measuring one or more location-dependent traits of each radio        signal transmitted by wireless terminal 101, in well-known        fashion, and of transmitting the measurements to location engine        113, and    -   d. transmitting one or more signals to wireless terminal 101 in        accordance with specific parameters (e.g., signal strength,        frequency, coding, modulation, etc.), in well-known fashion, and        of transmitting those parameters to location engine 113.

It will be clear to those skilled in the art how to make and use Wi-Fibase stations 103-1 and 103-2.

Wireless infrastructure 111 comprises a switch that orchestrates theprovisioning of telecommunications service to wireless terminal 101 andthe flow of information to and from location engine 113, as describedbelow and in the accompanying figures. As is well known to those skilledin the art, wireless switches are also commonly referred to by othernames such as mobile switching centers, mobile telephone switchingoffices, routers, etc.

Location-based application server 112 comprises hardware and softwarethat uses the estimate of the location of wireless terminal101—generated by location engine 113—in a location-based application, inwell-known fashion. Location-based applications are well-known in theart and provide services such as, and without limitation, E-911 routing,navigation, location-based advertising, weather alerts.

Location engine 113 is a data processing system that comprises hardwareand software that generates one or more estimates of the location ofwireless terminal 101 as described below and in the accompanyingfigures. It will be clear to those skilled in the art, after readingthis disclosure, how to make and use location engine 113. Furthermore,although location engine 113 is depicted in FIG. 2 as physicallydistinct from wireless infrastructure 111, it will be clear to thoseskilled in the art, after reading this disclosure, how to make and usealternative embodiments of the present invention in which locationengine 113 is wholly or partially integrated into wirelessinfrastructure 111. Location engine 113 comprises the location-traitdatabase and the geographic information system (GIS) database, which aredescribed in detail below.

Location Engine 113—

FIG. 2 depicts a block diagram of the salient components of locationengine 113 in accordance with the illustrative embodiment. Locationengine 113 comprises: processor 201, memory 202, and receiver andtransmitter 203, which are interconnected as shown. In accordance withthe illustrative embodiment of the present invention, location engine113 is a server computer; as those who are skilled in the art willappreciate after reading this specification, location engine 113 can bea different type of data-processing system or computing device.

Processor 201 is a general-purpose processor that is configured toexecute an operating system and the application software that performsthe operations described herein, including the operations described inFIG. 4 and other figures. Processor 201 is also capable of populating,amending, using, and managing a location-trait database and a GISdatabase, and of using one or more classification features as describedherein. It will be clear to those skilled in the art how to make and useprocessor 201.

In general, the location-trait database contains information for thepossible locations of wireless terminal 101 and the identity andlocation-dependent traits of radio signals as if wireless terminal 101were at each of those locations. In some embodiments, the location-traitdatabase is a database of maps (e.g., those that are described above andin FIG. 3, etc.) that associate each of a plurality of locations to oneor more predicted traits associated with a wireless terminal at thatlocation. It will be clear to those skilled in the art how to make anduse the location-trait database.

In general, the GIS database contains information for geographic region120, including without limitation, the physical characteristics of allof the structures (e.g., buildings, etc.) in geographic region 120. Itwill be clear to those skilled in the art how to make and use the GISdatabase.

Memory 202 is a non-volatile memory that is configured to store:

-   -   a. operating system 211, and    -   b. application software 212, and    -   c. database 213, comprising the location-trait database and GIS        database.        It will be clear to those skilled in the art how to make and use        memory 202.

Receiver and transmitter 203 is configured to enable location engine 113to receive from and transmit to wireless terminal 101, wirelessinfrastructure 111, location-based application server 112, and Wi-Fibase stations 103-1 and 103-2, in well-known fashion. It will be clearto those skilled in the art how to make and use receiver and transmitter203.

Radio Frequency Map of the Illustrative Embodiment—

FIG. 3 depicts a radio frequency (RF) map that represents a partitioningof geographic region 120 into 24 square locations. The maps aremaintained as part of the location-trait database, which is situated atlocation engine 113. In general, the map associates:

-   -   i. a plurality of possible locations of wireless terminal 101,        with    -   ii. a predicted value of a location-dependent trait for each of        the possible locations.

In other words, when wireless terminal 101 is at an unknown location, anempirical measurement of the location-dependent trait is a “fingerprint”or “signature” that can be used, in conjunction with the map, toestimate the location of the wireless terminal.

In accordance with the illustrative embodiment of the present invention,the location-dependent trait is the received signal strength as measuredin dBm, and each map associates each possible location of wirelessterminal 101 with the predicted received signal strength of one signalas transmitted from an antenna of a particular base station—in thiscase, base station 102-1—and as a function of the calendrical time, T,and the environmental conditions, N. With this in mind, FIG. 3 indicatesthe mapping of the signal radiated by the antenna of base station 102-1at Noon on a sunny day. Each of the base station 102-2 and 102-3antennas similarly has a map that associates each possible location ofwireless terminal 101 with the predicted received signal strength of onesignal as transmitted from the antenna of the particular base station.How each map is generated is described below and in FIG. 5.

It will, however, be clear to those skilled in the art, after readingthis specification, how to make and use alternative embodiments of thepresent invention in which one or more of the following predicted traitsare used, instead of or in addition to the trait of received signalstrength:

-   -   i. the predicted pathloss of all of the signals receivable by        wireless terminal 101 when wireless terminal 101 is at the        location, from all transmitters (e.g., base stations 102-1        through 102-3, commercial television, commercial radio,        navigation, ground-based aviation, etc.), as a function of the        calendrical time, T, and the environmental conditions, N; and    -   ii. the predicted pathloss of all of the signals transmitted by        wireless terminal 101 when wireless terminal 101 is in the        location as receivable at base stations 102-1 through 102-3, as        a function of the calendrical time, T, and the environmental        conditions, N; and    -   iii. the predicted received signal strength of all of the        signals transmitted by wireless terminal 101 when wireless        terminal 101 is in the location as receivable at base stations        102-1 through 102-3, as a function of the calendrical time, T,        and the environmental conditions, N; and    -   iv. the predicted received signal-to-impairment ratio (e.g.,        Eb/No, etc.) of all of the signals receivable by wireless        terminal 101 when wireless terminal 101 is in the location, from        all transmitters, as a function of the calendrical time, T, and        the environmental conditions, N; and    -   v. the predicted received signal-to-impairment ratio of all of        the signals transmitted by wireless terminal 101 when wireless        terminal 101 is in the location as receivable at base stations        102-1 through 102-3, as a function of the calendrical time, T,        and the environmental conditions, N; and    -   vi. the predicted received temporal difference of each pair of        multipath components (e.g., one temporal difference for one pair        of multipath components, a pair of temporal differences for a        triplet of multipath components, etc.) of all of the signals        receivable by wireless terminal 101 when wireless terminal 101        is in the location, from all transmitters, as a function of the        calendrical time, T, and the environmental conditions, N; and    -   vii. the predicted received temporal difference of each pair of        multipath components (e.g., one temporal difference for one pair        of multipath components, a pair of temporal differences for a        triplet of multipath components, etc.) of all of the signals        transmitted by wireless terminal 101 when wireless terminal 101        is in the location as receivable at base stations 102-1 through        102-3, as a function of the calendrical time, T, and the        environmental conditions, N; and    -   viii. the predicted received delay spread (e.g., RMS delay        spread, excess delay spread, mean excess delay spread, etc.) of        all of the signals receivable by wireless terminal 101 when        wireless terminal 101 is in the location, from all transmitters,        as a function of the calendrical time, T, and the environmental        conditions, N; and    -   ix. the predicted received delay spread (e.g., RMS delay spread,        excess delay spread, mean excess delay spread, etc.) of all of        the signals transmitted by wireless terminal 101 when wireless        terminal 101 is in the location as receivable at base stations        102-1 through 102-3, as a function of the calendrical time, T,        and the environmental conditions, N; and    -   x. the predicted received relative arrival times of two or more        multipath components of all of the signals receivable by        wireless terminal 101 when wireless terminal 101 is in the        location, from all transmitters (which can be determined by a        rake receiver in well-known fashion), as a function of the        calendrical time, T, and the environmental conditions, N; and    -   xi. the predicted received relative arrival times of two or more        multipath components of all of the signals transmitted by        wireless terminal 101 when wireless terminal 101 is in the        location as receivable at base stations 102-1 through 102-3, as        a function of the calendrical time, T, and the environmental        conditions, N; and    -   xii. the predicted round-trip time of all of the signals        transmitted and receivable by wireless terminal 101 through base        stations 102-1, 102-2, and 102-3, as a function of the        calendrical time, T, and the environmental conditions, N; and    -   xiii. the predicted round-trip time of all of the signals        transmitted and receivable by base stations 102-1, 102-2, and        102-3 through wireless terminal 101, as a function of the        calendrical time, T, and the environmental conditions, N; and    -   xiv. the identity of the base stations that provide        telecommunications service to the location, as a function of the        calendrical time, T, and the environmental conditions, N; and    -   xv. the identities of the neighboring base stations that provide        telecommunications service to the location, as a function of the        calendrical time, T, and the environmental conditions, N; and

the handover state (e.g., soft, softer, 1×, 2×, etc.) of wirelessterminal 101 and wireless telecommunication system 100 when wirelessterminal 101 is in the location as a function of the calendrical time,T, and the environmental conditions, N.

Operation of the Illustrative Embodiment—

FIG. 4 depicts a flowchart of the salient processes performed inaccordance with the illustrative embodiment of the present invention.

The processes performed by wireless telecommunications system 100 of theillustrative embodiment are depicted in the drawings (i.e., FIG. 4 andsubsequent figures) as being performed in a particular order. It will,however, be clear to those skilled in the art, after reading thisdisclosure, that such operations can be performed in a different orderthan depicted or can be performed in a non-sequential order (e.g., inparallel, etc.). In some embodiments of the present invention, some orall of the depicted processes might be combined or performed bydifferent devices. In some embodiments of the present invention, some ofthe depicted processes might be omitted.

At task 401, the location-trait database and the GIS database areconstructed and stored in memory 202 of location engine 113. Task 401 isdescribed in detail below and in FIG. 5.

At task 403, a characterization that is a composite of one or moreclassification features is generated, for a given combination ofclassification features, and stored in memory 202 of location engine113. In this sense, a “feature” is an individual measurable heuristicproperty of a phenomenon being observed and can be regarded as anexplanatory variable, as is known in the art. Task 403 is describedbelow and in FIG. 6.

In accordance with the illustrative embodiment of the present invention,a solution vector represents the characterization. The solution vectorrepresents how a given combination of features interact to determinewhen wireless terminal 101 classified as being indoors versus outdoors,when the solution vector and a decision threshold are applied toempirical data that are representative of the wireless terminal.

At task 405, location engine 113 collects empirical data on the radiosignals received and transmitted by wireless terminal 101. Task 405 isdescribed in detail below and in FIG. 9.

At task 407, location engine 113 generates an estimate of whetherwireless terminal 101 is indoors or outdoors. Task 407 is described indetail below and in FIG. 10.

At task 409, location engine 113 generates an estimate of the locationof wireless terminal 101 based on, among other things, the estimate ofwhether the wireless terminal is indoors or outdoors. Task 409 isdescribed in detail below and in FIG. 11.

At task 411, location engine 113 transmits the estimate of the locationof wireless terminal 101 generated at task 409 to location-basedapplication server 112 and/or to wireless terminal 101 for use in alocation-based application. In some embodiments of the presentinvention, location engine 113 transmits an indication of whetherwireless terminal 101 is determined to be indoors or outdoors, based onthe estimate that is generated as described below and in task 407. Itwill be clear to those skilled in the art how to enable embodiments ofthe present invention to perform task 411. After task 411 is completed,control passes back to task 405.

Task 401: Construct the GIS Database and the Location-Trait Database—

FIG. 5 depicts a flowchart of the salient processes performed inaccordance with task 401.

At task 501, the GIS database is constructed and stored in memory 202 oflocation engine 113. It will be clear to those skilled in the art how toaccomplish task 501.

At task 503, the location-trait database is constructed and stored intomemory 202 of location engine 113. As part of task 503, the identityof—and location-dependent traits for—each radio signal that each ofcellular base stations 102-1, 102-2, and 102-3, Wi-Fi base stations103-1 and 103-2 is expected to be able to receive from wireless terminal101, for each possible location of wireless terminal 101, is determinedin well-known fashion.

It will be clear to those skilled in the art how to accomplish task 503,and in accordance with the illustrative embodiment, this is accomplishedthrough a combination of “drive testing” (i.e., empirical datagathering) and radio-frequency propagation modeling. See for example andwithout limitation, U.S. Patent Application Publications 2008/0077356,2008/0077472, and 2008/0077516, which are incorporated by reference.

Task 403: Generate a Characterization, Determine a Decision Threshold—

FIG. 6 depicts a flowchart of the salient processes performed inaccordance with task 403.

At task 601, one or more classification features are calculated andstored into memory 202 of location engine 113, based on call dataoccurring over a predetermined interval. In some embodiments, the datacomprises measurements of radio signals received by a plurality ofwireless terminals over time and/or the identities of those radiosignals, and then acquired (e.g., via network measurement reports, etc.)and collected in the network over time. Each classification featurecharacterizes a location-dependent trait (e.g., signal strength, etc.),at least to the extent that a wireless terminal can be classified asbeing at an indoor location or at an outdoor location. In someembodiments of the present invention, such a location-dependent trait isdirectly measurable by a wireless terminal and/or by a base station,while in some other embodiments it is not directly measurable. In someembodiments of the present invention, each classification feature iscalculated as a single value per call. A characterization of aclassification feature across the aggregation of calls in a data set canbe represented as a histogram, as a cumulative distribution function(CDF), or as another suitable description, for example and withoutlimitation. Task 601 is described in detail below and in FIG. 7.

At task 603, one or more classification features are selected, to beused in determining whether wireless terminal 101 is indoors oroutdoors. For example and without limitation, the Neighbor Countfeature, the Neighbor RSSI Signal Level feature, and the NeighborSpanned-Area feature can be combined with one another, in anycombination, with improved detection results over at least some of theindividual classification features disclosed herein. In some embodimentsof the present invention, a combination of two or more classificationfeatures yields improved results over each constituent feature byitself, provided that the calculated measures of the classificationfeatures are not highly correlated with respect to one another. Theclassification features can be used in any combination with one anotherand/or with other indoor-outdoor classification features not depicted inFIG. 7.

At task 505, a characterization that is a composite of the selectedclassification features is generated and stored into memory 202 oflocation engine 113. The characterization and/or composite accounts forthe selected features, or for various aspects of those features, asdescribed below. In accordance with the illustrative embodiment, asolution vector is generated that is representative of thecharacterization. An often used method to fit data to known outcomes islinear least-squares (LLS). It only requires the computation of apseudo-inverse, for example using singular value decomposition (SVD). Inits purest form there are no parameters to tune. Since theindoor/outdoor classification problem has binary outcomes, the problemcan be posed as

$\begin{matrix}\begin{matrix}{{x = {\arg\;{\min\limits_{x}{{{A\; x} - b}}_{2}^{2}}}},} & {{A \in R^{n\; x\; m}},{b \in R^{n}},{x \in {R^{\pi\; 2}.}}}\end{matrix} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$In the foregoing equation, A is the matrix with the classificationfeature values (each call is a row, each feature a column), and b is thevector of zeros and ones, where an indoor call is set to one, and anoutdoor call to zero. To accommodate a bias in the feature values, thefirst column of A is set to all ones. The dimension n is the number ofcalls, and m is the number of features plus one (bias). The solutionvector x can be computed byx=A

b,  (Eq. 2)where (•)

denotes the pseudo-inverse.

In some embodiments of the present invention, another type of classifiercan be used such as, but not limited to, linear programming-basedclassification, a classification or decision tree, econometricmodeling-based classification, and so on.

At task 607, a decision threshold T_(D) is determined based on thegenerated solution matrix. An optimal threshold can be determined bylooking at the cumulative distribution function (CDF) of Ax for indoorand outdoor calls, and finding the largest gap, as depicted in FIG. 8.In FIG. 8, CDF 801 is a cumulative distribution function of outdoorcalls, and CDF 802 is a cumulative distribution function of indoorcalls. Gap 803 represents the largest gap between CDFs 801 and 803 andcan be used to determine the decision threshold.

In some other embodiments of the present invention, a different decisionthreshold can be determined, for example and without limitation byshifting the threshold it is possible to trade-off false positivesagainst false negatives, but at the expense of a lower overall gap. Itwill be clear to those skilled in the art, after reading thisspecification, how to account for the relative orientation of indoor andoutdoor data in the CDFs, in determining the threshold value.

Task 601: Calculate Classification Features—

FIG. 7 depicts a flowchart of the salient processes performed inaccordance with task 601. The classification features are defined andcalculated as follows.

At task 701, the Neighbor RSSI Signal Level feature is calculated. Therationale behind this feature is that because of the attenuation insidebuildings, a wireless terminal should report lower reported signalstrengths (RSSI) for the non-serving cells. To quantify this, the meanRSSI for a non-serving reported cell is computed, such as from slot 3 or4 assuming a network measurement report (NMR) whose reporting slots areordered from strongest to weakest reported neighbor. A low meanindicates being indoors. In some embodiments, the strongest non-servingcell measurement is used, while in some other embodiments anon-strongest, non-serving cell measurement is used. Additionally, to bemore generic, the RSSI can be computed as the mean over all of theslots. To capture the average RSSI, as well as the fluctuation, both themean and standard deviation are considered features of interest.

At task 703, the Neighbor RSSI Attenuation feature is calculated. Therationale behind this feature is to capture the attenuation in thepropagation path between the radio signal source (e.g., a base station,etc.) and a wireless terminal. The difference between the RSSI and thecell transmit power is a measure of the attenuation.

At task 705, the Neighbor Count feature is calculated. The rationalebehind this feature is that due to the attenuation inside buildings anindoor wireless terminal should see fewer cells than an outdoor wirelessterminal. Therefore, one can count how often a neighbor is missing inthe sequence of NMRs for slots 4, 5, and 6 for at least a certain numberof consecutive samples (a configurable window size). A high count shouldpredict being indoors. In addition, it is assumed that indoors awireless terminal will see the same cell more often. One could count thenumber of times a cell ID is reported consecutively, or even the numberof time a cell ID is reported consecutively in the same list position.Taking these two concepts into account results in a count of the numberof neighbors; this can be done per NMR, or per call. Also, the averagenumber of NMRs that a neighbor cell is listed (a measure of residencetime) can be computed. A large value for neighbor count or small valueof neighbor residence time indicates that a wireless terminal is seeingthe same cell less often.

At task 707, the Neighbor Spanned-Area is calculated. The rationalebehind this feature is that due to the attenuation inside buildings anindoor wireless terminal should only see cells that are closer.Therefore, the area of smallest convex hull enclosing the reported cellsis computed, for example using cell latitude/longitude data. A smallerarea indicates a higher probability of being indoors.

At task 709, the Server-to-Neighbor Distance is calculated. Similar tothe previous feature, a measure of how far away the wireless terminalcan see cells around it, is the mean distance from the serving cell (inlieu of the wireless terminal location) to the reported neighbors. Thisvalue can be scaled using the cell density, in order to reducesensitivity to the network topology.

At task 711, the Timing Advance feature is calculated. Although this canbe a coarse measure for areas with dense cell coverage (its value beinglimited to 0 or 1 for some data sets), a mean over a call may give amore accurate fractional value. A difference of this feature from theprevious feature is that it measures the distance from the serving cellto the wireless terminal.

At task 713, the Indoor-Cell Fraction feature is calculated. If theserving cell is a known indoor cell, it is likely that the wirelessterminal is also indoors. Also, this concept can be applied equally tothe neighbor cells.

Task 405: Collect Empirical Data on Radio Signals—

FIG. 9 depicts a flowchart of the salient processes performed inaccordance with task 405.

At task 901, each of cellular base stations 102-1, 102-2, and 102-3 andWi-Fi base stations 103-1 and 103-2 transmits the identity of eachsignal it has received from wireless terminal 101 and the measurementsof the location-dependent traits of those signals. In accordance withthe illustrative embodiment, task 901 is performed every 20milliseconds, but it will be clear to those skilled in the art how tomake and use alternative embodiments of the present invention thattransmit the measurements at other times.

At task 902, location engine receives the identities and measurementstransmitted at task 901.

At task 903, wireless terminal 101 transmits the identity of each signalit receives from cellular base stations 102-1, 102-2, and 102-3 andWi-Fi base stations 103-1 and 103-2 and the measurements of thelocation-dependent traits of those signals. In accordance with theillustrative embodiment, task 903 is performed every 20 milliseconds,but it will be clear to those skilled in the art how to make and usealternative embodiments of the present invention that transmit themeasurements at other times.

At task 904, location engine receives the identities and measurementstransmitted at task 903.

In accordance with the illustrative embodiment, tasks 901, 902, 903, and904 are performed continuously, concurrently, and asynchronously.

Task 407: Generate an Estimate of Whether Terminal 101 is Indoors orOutdoors—

FIG. 10 depicts a flowchart of the salient processes performed inaccordance with task 407.

At task 1001, location engine 113 generates an estimate of whetherwireless terminal is indoors or outdoors, based on the solution vector xand on α=[1, f₁, f₂, . . . f_(m-1)], where f₁ through f_(m-1) are thefeature values computed from the call data in accordance with task 601.Because indoor was set to one, and outdoor to zero, the classificationis based onif(αx>T), then indoor=true, else outdoor=true.  (Eq. 3)wherein T is the decision threshold determined earlier.

In some embodiments of the present invention, location engine 113 alsoestimates a probability that the wireless terminal is correctlyclassified as indoors (or outdoors), in well-known fashion. For exampleand without limitation, the estimated probability can be based on wherethe decision threshold is set in relation to the cumulative distributionfunctions in FIG. 8, which dictates how likely it is that the wirelessterminal is outdoors when it is decided that the wireless terminal isindoors, and vice-versa.

As those who are skilled in the art will appreciate, after reading thisspecification, the rest of the location estimation process can be basedon the probability estimate generated. For example, the locationestimation can react one way if the estimated probability of thewireless terminal being indoors is 95%, while the location estimationcan react a different way if the estimated probability is 50%.

In accordance with the illustrative embodiment of the present invention,the detection of whether wireless terminal 101 is indoors or outdoors isbased on a relatively short sequence of measurement data. However, asthose who are skilled in the art will appreciate after reading thisspecification, the detection of whether wireless terminal 101 is indoorsor outdoors can be based on one or more of i) one or more priordetections of the wireless terminal being indoors, ii) one or more priordetections of the wireless terminal being outdoors, and iii) one or moreprior estimates of the location of the wireless terminal, in anycombination thereof.

Generating an Estimate of the Location of Wireless Terminal 101—

FIG. 11 depicts a flowchart of the salient processes performed in task409—generating an estimate of the location of wireless terminal 101. Asan overview, Y probability distributions for the location of wirelessterminal 101 are generated for each of instants H₁ through H_(Y) in thetemporal interval ΔT, wherein Y is a positive integer, based oncomparing the measurements of traits associated with wireless terminal101 (i.e., the values obtained in task 904) at each of instants H₁through H_(Y), to predicted values for those traits at those times. Eachof the Y probability distributions provides a first estimate of theprobability that wireless terminal 101 is in each location at each ofinstants H₁ through H_(Y). This handling of the probabilitydistributions is described below and in task 1105.

In accordance with task 1101, location server 113 performs a techniquecalled “search area reduction” in preparation for task 1105. Tounderstand what search area reduction is and why it is advantageous, abrief discussion of task 1105 is helpful. In task 1105, location server113 performs a time-series analysis in order to estimate the probabilitythat wireless terminal 101 is in each location at each of instants H₁through H_(Y). This requires generating Y multi-dimensional probabilitydistributions, one for each of instants H₁ through H_(Y).

The process for generating each multi-dimensional probabilitydistribution can be computationally intensive and the intensity dependson the number of locations that must be considered as possible locationsfor wireless terminal 101. When the number of locations that must beconsidered is small, the process can be performed quickly enough formany “real-time” applications. In contrast, when the number of locationsthat must be considered is large, the process can often take too long.

Nominally, all of the locations in geographic region 120 must beconsidered because, prior to task 1101, wireless terminal 101 could bein any location out of possibly thousands, millions, or billions oflocations. The consideration of thousands, millions, or billions oflocations for each instant by location server 113 might take too longfor many real-time applications.

Therefore, to expedite the performance of task 1105, location server 113performs one or more computationally-efficient tests that quickly andsummarily eliminate many possible locations for wireless terminal 101from consideration, and, therefore, summarily set to zero theprobability that wireless terminal 101 is at those locations. Thisreduces the number of locations that must be fully considered in task1105 and generally improves the speed with which task 409 is performed.

For additional information in regard to location estimation, includingthe time-series analysis performed at task 1105, see for example andwithout limitation U.S. Pat. Nos. 6,944,465, 7,460,505, 7,383,051,7,257,414, 7,753,278, 7,433,695, 7,848,762, and 8,630,665, each of whichis incorporated by reference herein.

FIG. 12 depicts a flowchart of the salient processes performed inaccordance with task 1101—search area reduction. In some embodiments ofthe present invention, location server 113 uses additional techniques tothose described below, in order to perform search area reduction.

In accordance with task 1201, location server 113 designates a locationas improbable based on an estimate of wireless terminal 101 beingoutdoors, when the location is known to be indoors. The theoryunderlying this test is when the terminal is estimated to be outdoors,any indoor location is considered to be invalid. Although it is possiblethat the estimate of the wireless terminal being outdoors might bewrong, the possibility of this occurring can be minimized by selectingthe proper criteria (e.g., decision threshold, etc.) for generating theestimate in task 1001.

In accordance with task 1203, location server 113 designates a locationas improbable based on an estimate of wireless terminal 101 beingindoors, when the location is known to be outdoors. The theoryunderlying this test is when the terminal is estimated to be indoors,any outdoor location is considered to be invalid. Although it ispossible that the estimate of the wireless terminal being indoors mightbe wrong, the possibility of this occurring can be minimized byselecting the proper criteria (e.g., decision threshold, etc.) forgenerating the estimate in task 1001.

In some embodiments of the present invention, the probability valueestimated at task 1001 is taken into account. For example and withoutlimitation, a sufficiently high probability (e.g., 90%, 95%, 98%, etc.)that wireless terminal 101 is correctly classified might be required inorder to designate a location as improbable based on theindoors-outdoors criterion.

In some embodiments of the present invention, a location or portion of alocation is known to be indoors or outdoors based on the information(e.g., structures, etc.) contained in the GIS database constructed attask 501. Portions of a particular location might be indoors while otherportions of the location might be outdoors, instead of a particularlocation being either all indoors or all outdoors. In those embodiments,tasks 1201 and 1203 can be ignored or each location in thelocation-trait database constructed at task 503 in FIG. 5 can besubdivided for the purpose of tracking the indoor portions and outdoorportions of each location.

In some embodiments of the present invention, certain information can beinferred based on the estimate of the wireless terminal being indoors oroutdoors. For example and without limitation, a wireless terminal mightbe inferred as being at ground level when estimated to be outdoors,whereas the same is less likely to be true when the wireless terminal isestimated to be indoors (e.g., could be many floors above ground level,etc.).

In accordance with task 1205, location server 113 designates a locationas improbable based on the measurement of the location-dependent traitobtained at task 902 and/or 904. Various tests for designating alocation as improbable and that are based on the value of thelocation-dependent trait are described in U.S. Pat. No. 7,257,414, whichis incorporated herein by reference.

A location that that is designated as improbable at instant H, by one ormore of the foregoing processes is designated as improbable by task 1101at instant H.

FIG. 13 depicts a flowchart of the salient processes performed inaccordance with task 1103—map adjustment. In accordance with task 1103,location server 113 adjusts an RF map in preparation for task 1105,wherein the map is described above and in FIG. 3. In particular, server113 adjusts the map that associates: i) a plurality of possiblelocations of wireless terminal 101 with ii) a predicted value of thelocation-dependent trait associated with each of the plurality ofpossible locations of the wireless terminal. The map correlates each ofa plurality of locations to one or more predicted traits associated witha wireless terminal at that location. As described in FIG. 3, the mapsare stored as part of the location-trait database at location server113.

In accordance with task 1301, location server 113 adjusts the relevantmap stored in the location-trait database, based on an estimate ofwireless terminal 101 being indoors.

The theory underlying this adjustment is explained here. As describedabove, the map correlates each of a plurality of locations to one ormore predicted traits associated with a wireless terminal at thatlocation. Each location represented in the map, however, might compriseone or more portions that are indoors and one or more portions that areoutdoors. Additionally, the predicted value of each location-dependenttrait stored for that location might be representative of the indoorportions or of the outdoor portions, but not necessary of both.Therefore, the predicted values of one or more location-dependent traitsfor the location may have to be adjusted accordingly, based on whetherwireless terminal is estimated to be indoors or outdoors. For example,the predicted signal strength for a particular location might be toohigh for an indoor portion of the location or too low for an outdoorportion of a location, or both; consequently, the predicted signalstrength would have to be adjusted accordingly.

In some embodiments of the present invention, the probability valueestimated at task 1001 is taken into account. For example and withoutlimitation, a sufficiently high probability (e.g., 90%, 95%, 98%, etc.)that wireless terminal 101 is correctly classified might be required inorder to adjust the map based on the indoors-outdoors criterion.

Classification methods for the purpose of detecting whether a wirelessterminal is indoors or outdoors have been disclosed herein. However, asthose who are skilled in the art will appreciate after reading thisspecification, classification methods can also be applied for thepurpose of detecting a different type of status of the terminal, such aswhether the terminal is moving or stationary.

It is to be understood that the disclosure teaches just one example ofthe illustrative embodiment and that many variations of the inventioncan easily be devised by those skilled in the art after reading thisdisclosure and that the scope of the present invention is to bedetermined by the following claims.

What is claimed is:
 1. A method of estimating the location of a wirelessterminal, the method comprising: receiving, by a server computer, theidentities of one or more radio signals that are received by thewireless terminal; estimating, by the server computer, a probabilitythat the wireless terminal is indoors based on i) the identities of theone or more radio signals that are received by the wireless terminal andii) a characterization that is based on the amount of unique identitiesthat have appeared over a predetermined interval, wherein the uniqueidentities are of radio signals that have been received by a pluralityof wireless terminals; designating, by the server computer, at least oneof a plurality of possible locations of the wireless terminal asimprobable based on the estimated probability that the wireless terminalis indoors; and estimating, by the server computer, the location of thewireless terminal to be one of the plurality of possible locations ofthe wireless terminal not designated as improbable.
 2. The method ofclaim 1 wherein the characterization is a composite that accounts for i)the amount of unique identities that have appeared over thepredetermined interval and ii) the area of a convex hull spanned by thelocations of radio signal sources.
 3. The method of claim 1 wherein thecharacterization is a composite that accounts for i) the amount ofunique identities that have appeared over the predetermined interval andii) the area of a convex hull spanned by the locations of radio signalsources, and iii) the reported signal strength indicator (RSSI) of apredetermined slot in a measurement report, wherein the predeterminedslot corresponds to a measurement of a radio signal source of anon-serving cell.
 4. The method of claim 3 wherein the predeterminedslot further corresponds to a non-strongest measurement of a radiosignal source of a non-serving cell.
 5. The method of claim 1 furthercomprising: receiving, by the server computer, a measurement of alocation-dependent trait of a radio signal as received by the wirelessterminal; wherein the estimating of the location of the wirelessterminal is based on the measurement of the location-dependent trait ofthe radio signal.
 6. The method of claim 1 wherein the predeterminedinterval is the length of a call.
 7. The method of claim 1 wherein thecharacterization is a composite that accounts for i) the amount ofunique identities that have appeared over the predetermined interval andii) the amount of measurement reports in which measurements are presentfor a radio signal source that corresponds to a given unique identity ofthe unique identities that have appeared.
 8. A method of estimating thelocation of a wireless terminal, the method comprising: receiving, by aserver computer, the identity of a radio signal that is received by thewireless terminal; receiving, by the server computer, a measurement of alocation-dependent trait of a radio signal as received by the wirelessterminal; and estimating, by the server computer, a probability that thewireless terminal is indoors based on i) a characterization of a firstclassification feature, wherein the characterization is based onmultiple measurement reports that are transmitted by a plurality ofwireless terminals and accounts for both known indoor calls and knownoutdoor calls, and ii) a value of the first classification feature,wherein the value of the first classification feature corresponds to theidentity of the radio signal and is representative of the wirelessterminal; and generating, by the server computer, an estimate of thelocation of the wireless terminal based on i) the measurement of thelocation-dependent trait of the radio signal and ii) the estimatedprobability that the wireless terminal is indoors.
 9. The method ofclaim 8 further comprising: designating, by the server computer, atleast one of a plurality of possible locations of the wireless terminalas improbable based on the estimated probability that the wirelessterminal is indoors; wherein the estimate of the location of thewireless terminal is one of the plurality of possible locations of thewireless terminal not designated as improbable.
 10. The method of claim8 further comprising: designating, by the server computer, at least oneof a plurality of possible locations of the wireless terminal asimprobable; wherein the estimate of the location of the wirelessterminal is determined to be one of the plurality of possible locationsof the wireless terminal not designated as improbable, and is based onthe measurement of the location-dependent trait of the radio signal andon an adjusted database value of the location-dependent trait, whereinthe adjusted database value is based on the estimated probability thatthe wireless terminal is indoors.
 11. The method of claim 8 wherein thecharacterization accounts for the amount of unique identities that haveappeared over a predetermined interval, wherein the unique identitiesare of radio signals that have been received by a plurality of wirelessterminals.
 12. The method of claim 11 wherein the characterization is acomposite that accounts for i) the amount of unique identities that haveappeared over the predetermined interval and ii) the area of a convexhull spanned by the locations of radio signal sources.
 13. The method ofclaim 8 wherein the estimating of the probability that the wirelessterminal is indoors is further based on ii) a second classificationfeature that accounts for the reported signal strength indicator (RSSI)of a predetermined slot in a measurement report, wherein thepredetermined slot corresponds to a measurement of a radio signal sourceof a non-serving cell.
 14. The method of claim 13 wherein thepredetermined slot further corresponds to a non-strongest measurement ofa radio signal source of a non-serving cell.
 15. A method of estimatingthe location of a wireless terminal, the method comprising: receiving,by a server computer, the identity of a radio signal that is received bythe wireless terminal; receiving, by the server computer, a measurementof a location-dependent trait of a radio signal as received by thewireless terminal; estimating, by the server computer, a probabilitythat the wireless terminal is indoors based on i) a characterization ofa first classification feature, wherein the characterization is based onmultiple measurement reports that are transmitted by a plurality ofwireless terminals, and ii) a value of the first classification feature,wherein the value of the first classification feature corresponds to theidentity of the radio signal and is representative of the wirelessterminal; designating, by the server computer, at least one of aplurality of possible locations of the wireless terminal as improbablebased on the estimated probability that the wireless terminal isindoors; and generating, by the server computer, an estimate of thelocation of the wireless terminal as being one of the plurality ofpossible locations of the wireless terminal not designated asimprobable, based on the measurement of the location-dependent trait ofthe radio signal.
 16. The method of claim 15 wherein thecharacterization accounts for the amount of unique identities that haveappeared over a predetermined interval, wherein the unique identitiesare of radio signals that have been received by a plurality of wirelessterminals.
 17. The method of claim 16 wherein the characterization is acomposite that accounts for i) the amount of unique identities that haveappeared over the predetermined interval and ii) the area of a convexhull spanned by the locations of radio signal sources.
 18. The method ofclaim 15 wherein the generating of the estimate of the probability thatthe wireless terminal is indoors is further based on ii) a secondclassification feature that accounts for the reported signal strengthindicator (RSSI) of a predetermined slot in a measurement report,wherein the predetermined slot corresponds to a measurement of a radiosignal source of a non-serving cell.
 19. The method of claim 18 whereinthe predetermined slot further corresponds to a non-strongestmeasurement of a radio signal source of a non-serving cell.